The defense community is in a difficult position with respect to artificial intelligence. The capability gains are real, the adversaries are investing, and the pressure to adopt is significant. At the same time, the dominant commercial AI stack was built on assumptions — centralized inference, hosted weights, opaque supply chains, continuous telemetry — that are not compatible with how a serious defense program operates.
Neuraphic builds for that gap. Our systems are designed from first principles to be operable in environments where data cannot leave the boundary, where adversaries will actively probe the model, and where the organization deploying the system must retain full control over its weights, its training data, and its decision trace.
The problem with borrowed AI
A model you cannot inspect is a model you cannot trust. A model that phones home is a model that leaks. A model whose training data was selected by someone else is a model whose blind spots were selected by someone else. These are not abstract concerns for defense users; they are the core reason many AI initiatives inside national security organizations have stalled at the pilot stage.
We think the way forward is not to retrofit hosted commercial models into sensitive environments. It is to build models and infrastructure that assume sovereignty from the first line of code — and to accept the engineering cost that assumption imposes.
"Defensive AI has to survive contact with a competent adversary. A model you cannot inspect is a model you cannot trust."
Adversarial robustness as a baseline
Defensive AI has to survive contact with a competent adversary. That is a higher bar than most AI systems are built to meet, and it requires explicit investment at every layer: training data that has been examined for poisoning, inference pipelines that resist prompt injection, agents that refuse to act outside their declared scope, and evaluation frameworks that keep pace with the attacks.
Prion is an inference-time defense layer for language models, designed to classify and neutralize adversarial inputs before they reach the model making the decision. It is the same layer we use on our own systems, and it is available for deployment on customer infrastructure.
Continuous architecture defense
Beyond the model edge, Claeth operates as an autonomous cybersecurity analyst for classified environments. Capable of reasoning about complex infrastructure without requiring outbound internet telemetry, Claeth continuously maps dependencies, audits air-gapped code deployments, and mathematically verifies vulnerability patches inside the sovereign boundary.
Air-gapped deployment
We support deployment into disconnected environments. That means signed artifacts, offline update paths, no telemetry required for operation, and the ability to run our inference stack on customer-owned hardware without a dependency on Neuraphic-hosted services. Where classification and cross-domain requirements are in play, we work with the customer's accreditation path rather than asking them to adopt ours.
Our canonical architecture already separates the model layer from the control plane, which makes isolating and certifying a deployment substantially cheaper than retrofitting a product that was built cloud-first.
Compliance and trust
We are a U.S.-incorporated company and we build with the expectation that our defense users will ask hard questions about supply chain, data handling, and personnel. We will answer those questions directly. We do not yet hold every attestation that a long-term defense deployment would eventually require, and we are working toward the ones that apply — candidly, on a published timeline, through our Trust Center.
We do not take on work we cannot do responsibly. Our Responsible Scaling Policy and safety philosophy apply to every deployment, including deployments into defense contexts. The point of those documents is that they are not adjusted for the customer.
Get started
Program offices and integrators evaluating AI infrastructure for national security missions can reach us at enterprise@neuraphic.com. We prefer technical conversations early, and we are comfortable engaging under appropriate non-disclosure before any commitment is made.